Currency Devaluation and Trade Balance Nexus: A Test of

ISSN: 2695-2246 STRIDES EDUCATIONAL FOUNDATION

This paper and more can be downloaded without charge from http://www.bequarterly.rysearch.com

Are Latifat Olasubomi: [email protected] a. Research Officer, Small Business & Informal Economic Research Group, Department of

Economics, University of Lagos Akoka, Akoka Lagos

How to cite this article: Are, L. O., (2019). Currency Devaluation and Trade Balance Nexus: A Test

of Marshall-Lerner Condition in Nigeria. BizEcons Quarterly, 4, 23–43.

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Are Latifat Olasubomia

Currency Devaluation and Trade Balance Nexus: A Test of

Marshall-Lerner Condition in Nigeria

A b s t r a c t

Devaluation on the trade balance in recent times emerged as

a vital policy issue in Nigeria due to the recent economic

crisis. This paper examines the nexus between devaluation

and Nigeria’s trade balance by empirically investigating the

Marshall-Lerner condition using annual data covering the

period of 1986-2015. It also examines the dynamics of the

relation between Nigeria’s trade balance and the real

exchange rate. The Engel-Granger OLS-based

Cointegration technique and the Error Correction

Mechanism (ECM) modelling technique were employed to

conduct these analyses. Findings suggest that the Marshall-

Lerner condition is not satisfied in Nigeria and that real

exchange rate has no significant effect on the trade balance

both in the long-run and short-run. Also, findings revealed

that income effects dominate price effects on trade in

Nigeria.

Received: December 28, 2018

Revised:

February 3, 2019

Accepted:

June 1, 2019

Keywords:

Exchange rate

Currency devaluation

Trade balance

JEL Classification:

B41

C21

F31

1. Introduction

In many countries, including Nigeria, the deliberate downward adjustment of the

official exchange rate, has been used to address various macroeconomic issues such

as increasing international competitiveness, correcting a currency overvaluation,

reducing a current account deficit, and in most cases, improving the trade balance

(see Loto, 2011: 624-633). It is also stipulated in the International Monetary Fund

(IMF) policy that a country should devalue its currency to solve any essential

disequilibrium issues in its balance of payments.

Volume 4 (2019) • 23 – 43

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mailto:[email protected]



Are B I Z E C O N S Q U A R T E R L Y

• 24 •

From 1960 to early 1970s, Nigeria was predominantly an agrarian economy

depending on the agricultural sector. During this period, a fixed exchange rate regime

was practised with the Naira fixed at par to the British Pounds Sterling. After the

discovery of Oil in Nigeria, followed by the oil boom period in the early 1970s, the

oil sector relegated the agricultural and other sectors of the economy to the ground to

become the backbone of the economy accounting for 90% of export, 80% of

government revenue and 85% of foreign exchange earnings. However, when the oil

price crashed, the economy was faced with serious internal and external imbalances

that led to macroeconomic instability. In response to this macroeconomic instability,

the Federal Government adopted the Structural Adjustment Programme (SAP)

proposed by the IMF in 1986 to tackle these crises.1 Following the adoption of the

SAP, the Naira was devalued to achieve internal and external balance in the medium

term, which marked the beginning of the flexible exchange rate regime in Nigeria.

After the implementation of SAP, the economy did achieve both a favourable balance

of payments position and an improved macroeconomic condition.

In 2014, history repeated itself and the international oil price crashed from as high as

$115 per barrel in June 2014 to as low as $35 at the end of February 2016, which

plunged the economy into recession (Evans, 2019). Because proceeds from oil export

revenue still account for more than 70% of government revenues and about 90% of

foreign exchange earnings, this crash engendered massive distortions in the economy.

However, even though this current crisis is similar to what was experienced in the

early 1980s, the Central Bank of Nigeria (CBN) resisted against devaluing the Naira

as advised by some economists and the IMF. The argument put forward by the CBN

governor and others who were against the devaluation of the Naira was that the

structure of the present Nigeria economy and Foreign trade structure did not support

devaluation improving the trade balance and creating external balance.

In Nigeria foreign trade structure is characterized by major export of crude oil,

however, crude oil prices do not respond to policy instruments especially in the short

run because they are inelastic (Odili, 2007: 21). This controversial debate on the

economic notion of using a devaluation to achieve economic growth by improving

the trade balance, before now, has attracted the attention of many researchers who

have addressed this contentious issue by evaluating the impact of devaluation on the

trade balance either by directly examining the impact of devaluation on trade balance

or by investigating the Marshall-Lerner condition. There are various empirical studies

that have attempted to examine the validity of Marshall-Lerner condition in Nigeria

with no consensus as to what the impact of devaluing the Naira will be on the trade

balance. Hence, the need to re-evaluate how and what is the impact of devaluing the

Naira on Nigeria’s trade balance.

1 The Structural Adjustment Programme is as economic reform package aimed at enhancing

local productivity, achieving balance of payments equilibrium, expanding economic base and

boosting the growth potential of developing countries.

Currency Devaluation and Trade Balance Nexus: A Test of Marshall-Lerner Condition in Nigeria

• 25 •

Therefore, this paper seeks to contribute to the existing literature by critically re-

examining the impact of devaluing the Naira on Nigeria’s trade balance. This was

done by empirically investigating the M-L condition using Nigeria’s total imports

and exports data and also by empirically analysing the long-run and short-run

relationship between the trade balance and the real exchange rate.2

The remaining sections of this paper are organised as follows: section II provides a

review of relevant existing literature; section III explains the data and method;

section IV discusses the empirical results while section V provides the conclusion

and policy implications.

2. Literature Review

2.1 Theoretical Framework

The question of how devaluation impacts trade (current account) balance or the

balance of payments in general has been addressed over the decade using these three

different approaches; the elasticity approach to trade balance, the absorption

approach to trade balance, and the monetary approach to balance of payments. This

paper employed the conventional elasticity approach to trade balance.

The traditional elasticity approach is the most commonly adopted approach by many

empirical studies in assessing the impact of a devaluation on the trade balance. The

elasticity approach focuses on the degree of responsiveness of import and export

volumes to a change in relative prices through the exchange rate. This approach was

propounded by Bickerdike (1920), and popularised by Robinson (1937) and Metzler

(1948).

According to the elasticity approach, devaluation has two effects on the trade balance

which are the value effect and volume effect, and the net effect depends on which of

the two effects dominate. Following a devaluation, the domestic price of imports and

exports are affected as follows: The foreign price of domestic export falls relative to

foreign prices, while the foreign price of imports rises relative to domestic prices,

both in proportion to the devaluation. This makes the domestic exports cheaper for

foreigners and foreign imports more expensive for domestic residents. The volume

effect occurs when foreigners demand more of the domestic export which has

become relatively cheaper while domestic residents reduce their demand for foreign

import and substitute that with cheaper domestic good.

However, while less is demanded of foreign goods, more money is spent to purchase

the same quantity or less due to the fall in the purchasing power of the domestic

currency relative to the foreign currency. This translates into the value effect that

only worsens the current account. Therefore, if the value effect dominates the volume

effect after a devaluation, which is mostly the case in the short-run, the trade balance

2 Exports in Nigeria are usually segregated into oil and non-oil with oil exports accounting for

more than 90% of the total exports. Also, the trade balance can be divided into trade balance

with oil and trade balance without oil.


• 26 •

will worsen. On the other hand, should the volume effect dominate, which is more

likely to occur in the long-run due to an adjustment lag that takes place in the short-

run following a devaluation, the trade balance will improve.

According to Alexander (1952: 263), the effect that dominates both on the export and

import side depends on these four elasticities; (1) elasticity of foreign demand for the

devaluing country’s export, (2) elasticity of domestic demand for foreign import, (3)

elasticity of domestic supply of export and (4) elasticity of foreign supply of import.

Based on these four elasticities, the Bickerdike-Robinson-Metzler (BRM) model

provided a sufficient condition (BRM condition) for a trade balance improvement

following a devaluation. From the BRM condition, Marshall (1923) and Lerner

(1944) described the condition under which a devaluation will promote the trade

balance based on the following assumptions: that the price elasticity of supply at

home and abroad both equal infinity, ignoring the monetary effects of exchange rate

variations, assuming an initial balance of trade and that import, and export depends

on exchange rate variations (see Loto, 2011: 624-633).

Hence, the M-L condition stipulates that, devaluation will improve the trade balance

if the export and import volumes are sufficiently elastic with respect to relative

prices. The condition requires that the sum of the trade elasticities to changes in

relative prices be greater than unity in absolute terms, and the satisfaction of this M-L

condition guarantees devaluation will improve the trade balance. The M-L condition

can be mathematically expressed as𝜂𝑥 + 𝜂𝑚 > 1, where 𝜂𝑚and 𝜂𝑥are the price

elasticity of demand for imports and exports respectively.3

2.2 Empirical Literature Review

In international economics, the traditional elasticity approach to devaluation has been

the most widely adopted theoretical approach to evaluating the impact of devaluation

on the trade balance. This approach forms the basis for the M-L condition which is

what most studies empirically investigate for different countries. The M-L condition

requires that the sum, in absolute terms, of the import and export price elasticities be

greater than one for the trade balance to be improved by devaluation.

Robinson (1937) described the condition under which the trade balance will be

improved due to a change in the exchange rate and also derived the corresponding

elasticities which have formed the foundation for various empirical analysis ever

since (see Eita, 2013: 511-518). Several studies have employed different techniques

in estimating these elasticities to test if M-L condition holds for different countries

with mixed results.

Goldstein and Khan (1978) used the full-information maximum likelihood method to

estimate reduced form export demand and export supply functions simultaneously for

eight industrial countries (viz., Belgium, France, Germany, Italy, Japan, the

3A step by step derivation of the mathematical expression of the M-L condition is provided in

Appendix C.


• 27 •

Netherlands, the United Kingdom, and the United states) using quarterly data over

the period of 1955-1970. The price elasticities estimated from these functions were

combined with the elasticities of demand for import and export estimated in

Goldstein and Khan (1976) for the same countries. The combined results provided

support for the M-L condition for all countries except for Japan. However, a study by

Sinha (2001) estimated the price and income elasticities for five Asian countries

(Japan, Philippines, Sri Lanka, India and Thailand) using different techniques and

found that the M-L condition is met for four of those countries including Japan.

Miles (1979) established a direct link between the trade balance and the exchange

rate and employed several econometric techniques to examine the effect of

devaluation on the trade balance for 16 devaluations in 14 countries, both DCs and

LDCs, over the period of 1967-1962. Miles concluded that devaluation has no

significant improvement effect on the trade. In contrast, Himarios (1989), after

criticising Miles's model of using the trade balance to income ratio has the dependent

variable rather than the trade balance, employed the OLS method to analyse 60

devaluation experience for 27 countries from two different samples: 1953–73 and

1975–84. The author found that changes in the relative price as a result of

devaluation, has significantly positive effect on the trade balance.

Taking a step further from what was done by these two studies, Bahmani-Oskooee

(1994) employed the cointegration and error correction modelling technique to re-

examine the long-run and short-run relation between the trade balance and the real

effective exchange rate of 19 DCs and 22 LDCs using quarterly data from 1971 to

1990. The result from this study showed that the trade balance and the exchange rate

have no significant long-run relationship for most of the countries which is consistent

with the result of Miles (1979).

Arinze (1994) employed the cointegration technique to test if a relation exists

between the trade balance and the exchange rate in the long run for nine Asian

countries using quarterly data from 1973Q1 to 1991Q1. Contrary to the results above,

this study found a significant long-run equilibrium between the trade balance and the

real exchange rate. Likewise, Bahmani-Oskooee (1998), employed a Maximum-

likelihood cointegration technique to estimate the trade elasticities for six LDCs

using quarterly data over the period of 1973I-1990IV. From those estimates, it was

concluded that the trade balance of these counties did not improve by devaluing their

currencies.

The literature review for Nigerian studies indicate lack of consensus on how

devaluation will impact the trade balance and if the M-L condition does or will holds

in Nigeria. For example, Odili (2014) employed an autoregressive distributed lag

(ARDL) cointegration estimation technique to analyse the long-run and short-run

relationship that exists between the balance of payments and some macroeconomic

variables from 1971-2012. While using the export and import estimates to test if the

M-L condition holds for Nigeria, the study revealed that a positive and significant


• 28 •

relationship exists between the exchange rate and the trade balance and found support

for the M-L condition in Nigeria.

In corroboration with this finding is the result of Ogbonna (2011) which found that

M-L condition hold for Nigeria and devaluation can be used to improve the trade

balance. Ogbonna (2011) employed the Johansen cointegration approach to estimate

a multivariate cointegration system to examine the empirical relationship between the

real exchange rate and the trade balance both in the long-run and short-run, over the

period of 1970-2005. Results from this study confirm that the M-L condition holds

for Nigeria.

Similarly, Igue and Ogunleye (2014), using the OLS method, Johansen cointegration

technique and vector error correction methodology, estimated an import demand

equation, export supply equation and a trade balance equation using quarterly data

series from 1985Q1 to 2010Q4. The results from the study confirmed that the M-L

condition is satisfied for Nigeria and also established that if the exchange rate is

devalued by just 1%, the trade balance will improve by at least 1.16% implying that

devaluation will significantly improve the trade balance in the long-run.

In contrast, Ogundipe, Ojeaga and Ogundipe (2013) empirically investigated the

impact of devaluing the Naira on the trade balance over the period of 1970-2010

using a Johansen cointegration and variance decomposition analyses. They found that

exchange rate induces an inelastic and significant effect on the trade balance and

therefore worsens the trade balance in the long-run. In support, Loto (2011)

investigated the M-L condition in terms of total imports and non-oil exports during

the period of 1986-2010, using the OLS method. The results confirmed that

devaluation cannot improve the trade balance (without oil) due to the structure of the

Nigerian economy and its foreign trade.

Also, Eke, Eke and Obafemi (2015), using larger time period covering 1970 to 2012,

spanning both the pre and post 1986 devaluation of the Naira, used the Johansen

cointegration technique and error correction mechanism modelling technique to

estimate the effect of on the trade balance of exchange rate. The estimated results

from this study suggested that devaluation cannot lead to an improvement of the trade

balance. Thus, providing support for other negative results.

Similarly, Anoke, Odo and Ogbonna (2016), examined the effect of a depreciation on

the trade balance using the cointegration test, Vector Error Correction Model and

other technique from 1986-2014. The authors concluded from their estimates

Nigeria’s trade balance cannot be improved in the short run from depreciation of the

Naira, as only countries that are initially export based before deprecation only benefit

from such depreciation which is similar to the point raised by Loto (2011), as an

argument of lack of support for M-L condition in Nigeria.

However, this ongoing debate on the relationship that exists between the Nigeria’s

trade balance and a devaluation of the Naira calls for a re-evaluation of this


• 29 •

relationship and the M-L condition given the current situation of the Nigerian

economy.

3. Data and Methods

3.1 Models

The elasticity approach to the trade balance will be employed in preference to the

other approaches, because the focus is to empirically examine the M-L condition and

it is the conventional approach generally adopted in the literature to investigate the

M-L condition (see, Ogbonna, 2011; Igue et al. 2014). Hence, employing this

approach provides a basis for situating our findings within the existing literature.

To estimate the necessary elasticities, both an import demand and export supply

model were specified. In addition, a trade balance model was also formulated and

estimated as specified in existing literature (e.g. see Igue et al, 2014: 347-358)

The import demand model is expressed as a function of domestic income and the

relative price of imports, while the export supply model is expressed as a function of

world income and relative export prices as shown below in their most general forms:

The import demand model can be specified in a functional form as;

𝑀𝑡 = 𝑓(𝑌𝑡 , 𝑅𝑃𝑀𝑡) (1)

𝑋𝑡 = 𝑓(𝑌𝑊𝑡 , 𝑅𝑃𝑋𝑡) (2)

To have the estimated coefficients interpreted in elasticity form, the models are

expressed in a log-linear form as long-run import and export equations:

𝑙𝑛𝑀𝑡 = 𝛽0 + 𝛽1𝑙𝑛𝑌𝑡 + 𝜂𝑚

𝑙𝑛𝑅𝑃𝑀𝑡 + 휀𝑡 (3)

𝑙𝑛𝑋𝑡 = 𝛿0 + 𝛿1𝑙𝑛𝑌𝑊𝑡 + 𝜂𝑥

𝑙𝑛𝑅𝑃𝑋𝑡 + 𝜇𝑡 (4)

where 𝑀𝑡and 𝑋𝑡 are the volume of domestic imports and exports during year 𝑡 to the

world respectively; 𝑌𝑡 and 𝑌𝑊 are the domestic and world income in period 𝑡

respectively; 𝑅𝑃𝑀𝑡and 𝑅𝑃𝑋𝑡are the relative price of imports and exports in period

𝑡 respectively; 휀𝑡and 𝜇𝑡are the error terms for which standard assumptions are made;

𝛽1& 𝛿1 represent the elasticities with respect to income, while 𝜂𝑚

& 𝜂𝑥

, represent the

elasticities with respect to relative prices.

These equations will be used to estimate demand elasticities for both imports and

exports with respect to relative prices and income on an aggregate and bilateral basis.

It is expected that:

𝜂𝑚 < 0, since a devaluation should cause the prices of imports to rise relative to

domestic prices, leading to an increase in relative price of imports.

𝜂𝑥 < 0 because devaluation should make domestic export prices fall relative to the

world’s export prices.


• 30 •

𝛿1 > 0 such that when the world’ income increase, it is expected to increase the

demand for Nigerian exports.

𝛽1 >< 0, as the expected sign is ambiguous. The sign is generally taken to be

positive because as domestic income increases, demand increase and the volume of

import demand increases. However, it could be negative if the rise in real income is

due to increase in national output or production, especially when these outputs are

substitutes for imported goods. Then, imports will decrease as the domestic income

increases.

In other for the M-L condition to be satisfied it is expected that the absolute value of

the sum of the import and export demand elasticities be greater than one. Hence, we

test the following hypothesis;

Null hypothesis = 𝐻0: |𝜂𝑚

+ 𝜂𝑥

| = 1

Alternative hypothesis = 𝐻1: |𝜂𝑚

+ 𝜂𝑥

| > 1

Another model formulated and estimated in this paper is a trade balance model which

does not formally test for the M-L condition. But it used to determine the effect of the

exchange rate on the trade balance.

However, there has been debate as to the appropriate measure of the trade balance.

Several studies e.g. (see Bahmani-Oskooee, 1991:403-407) measured the trade

balance as the ratio of export to imports and has convincingly argued that it is a more

convenient and appropriate measure as it is insensitive to the units of measurements

of exports and imports and also insensitive to whether the imports and exports value

are in domestic or foreign currency. This method of measuring the trade balance is

also employed in this study and expressed thus in line with Boyd and Smith (2001:5):

𝑇𝐵𝑡 = (𝑋𝑡𝑃𝑡)

(𝑀𝑡𝐸𝑡𝑃𝑡∗)⁄ (5)

where, 𝑇𝐵𝑡 is the trade balance measured as the ratio of the export volume

𝑋𝑡 multiplied by the domestic prices 𝑃𝑡, to import volume 𝑀𝑡 multiplied by the

foreign prices 𝑃𝑡∗ and the nominal exchange rate 𝐸𝑡.

The trade balance equation to be analysed in line with the M-L condition prescription

is specified thus:4

𝑙𝑛𝑇𝐵𝑡 = 𝜃0 + 𝜃1𝑙𝑛𝑅𝐸𝑅𝑡 + 𝜃2𝑙𝑛𝑌𝑡 + 𝜃3𝑙𝑛𝑌𝑊𝑡 + 𝜖𝑡 (6)

Where 𝜃 = (𝛽0 − 𝛿0); 𝜃1 = (𝜂𝑚 + 𝜂𝑥 − 1); 𝜖𝑡 = (휀𝑡 − 𝜇𝑡), 𝑌𝑡 is the domestic

income in period 𝑡; 𝑌𝑊𝑡 is the world income in period 𝑡; 𝑅𝐸𝑅𝑡 is a trade-weighted

real effective exchange rate, defined as the foreign price of the Naira in period t. For

the trade balance to improve from devaluation, it is expected that;

4The mathematical derivation of the trade balance equation estimated is provided in appendix

C.


• 31 •

𝜃1 < 0 , is expected to be significantly negative since a decrease in 𝑅𝐸𝑅𝑡𝑡 reflects a

devaluation of the Naira.

𝜃2 >< 0, because an increase in domestic income should increase import demand if

the increase in income is not as a result of an increase in the production of import

substitutes goods which will, in turn, worsen the trade balance. However, if increase

in the production of import substitutes brought about the increase in income, this will

improve the trade balance by reducing the volume of import demands.

𝜃3 > 0, since an increase in world’s income is expected to increase the demand for

Nigeria export and improve the trade balance.

3.2 Data

Data employed in this study were collected over the period covering 1986-2014

inclusive. The choice of 1986 was influenced by the fact that it marked the period

when the Naira was first devalued following the adoption of the IMF’s Structural

Adjustment Programme (SAP). This ensures our empirical analysis is over a period

governed by a flexible exchange rate regime in Nigeria. The data collected, and their

sources include: Nigeria’s total imports and exports volume index sourced from

UNCTAD STAT; Nigeria’s real GDP in constant local currency units; the world’s

real GDP in constant 2005 US$; the GDP deflator for Nigeria drawn from IMF

international Financial Statistics; the world export unit index sourced from IMF

international Financial Statistics; and the real effective exchange rate for Nigeria

collected from the World Bank database. Some of the variables constructed from

these data are defined as:

𝑙𝑛𝑅𝑃𝑀𝑡 which is the natural logarithm of the relative price of imports and it is

defined as the ratio of Nigeria import prices to the Nigeria GDP price deflator.

𝑙𝑛𝑅𝑃𝑋𝑡 which is the natural logarithm of the relative price of exports defined as the

ratio of Nigeria’s export price to World export unit index.

𝑙𝑛𝑅𝐸𝐸𝑅𝑡 which represents the natural logarithm of the real effective exchange rate of

Nigeria is constructed by dividing the nominal effective exchange rate (a measure of

the value of a currency against a weighted average of several foreign currencies) by a

price deflator or index of costs (World Bank database).

3.3 Methodology

Annual time series data are used in this study to empirically analyse equations (3), (4)

and (6) specified above. These time series data are examined to avoid encountering

common problems that time series data generally possess (Evans et al, 2018). The

most crucial of those problems for this analysis is the problem of non-stationarity.

Time series are said to be nonstationary because their probability distribution are not

stable overtime and as such exhibit a stochastic trend. A non-stationary time series

data is practically invalid for forecasting purpose. Also, regressing two or more

nonstationary time series may lead to spurious regression.


• 32 •

Unit roots tests were conducted to confirm the stationarity of the individual data

series and also to determine the order of integration for each variable. The Dickey-

Fuller unit root test was generally employed, and the Augmented Dickey-Fuller test

was used in few cases where the error term in the equations are serially correlated.

The presence of unit-root in each variable are tested for using the following equation:

(1 − 𝐿)𝑙𝑛𝑍𝑡 = 𝜌0 + 𝜌1𝑙𝑛𝑍𝑡−1 + 𝜇𝑡 (7)

where, (1 − 𝐿)𝑙𝑛𝑍𝑡 is the first difference of the log of the time series; 𝑙𝑛𝑍𝑡−1is a one

period lag of the time series; 𝑡 is a trend variable; 𝜌0 is a constant term and 𝜇𝑡 is a

stochastic error term. Equation (7) depicts a random walk with drift while and the

decision of whether a time series is stationary or not depends on the estimates of 𝜌1.

A time series free of unit root will have a significant estimate of 𝜌1 < 0, whereas, a

time series with unit root or stochastic process will have an estimate of 𝜌1 that is

statistically not different from zero. Therefore, the following hypotheses are put to

test using the appropriate critical values:

Ho: 𝜌1 = 0

Ha: 𝜌1 < 0

These tests were conducted using the formula for a t-test. However, the distribution

of these tests is non-standard and special critical values devised by Dickey-Fuller

must be used for inference purposes.

After identifying non-stationary data and determining their level of stationarity of the

data, cointegration test is conducted to determine if a long-run relationship exists

between the set of nonstationary variables. A residual based cointegration test based

on the Dickey-fuller test is employed to determine if a linear combination of these

nonstationary series cancels out the stochastic trends in the series. The residual based

Dickey-Fuller test for cointegration is employed to test for cointegration between the

variables in the import demand, export supply and trade balance equations. This is

done by regressing each of the three equations using OLS method and subjecting the

residuals from each set of regression to a unit root test to determine if the residual is

integrated of a lower order of integration than the order of integration of the variables

in the long-run levels model. The OLS regression technique is applied to the

following equation to estimate the residuals;

𝑙𝑛𝑀𝑡 = 𝛽0 + 𝛽1𝑙𝑛𝑌𝑖𝑡 + 𝛽2𝑙𝑛𝑅𝑃𝑀𝑖𝑡 + 𝜋𝑡 (8)

𝑙𝑛𝑋𝑡 = 𝛿0 + 𝛿1𝑙𝑛𝑌𝑊𝑖𝑡 + 𝛿2𝑙𝑛𝑅𝑃𝑋𝑖𝑡 + 𝜌𝑡 (9)

𝑙𝑛𝑇𝐵𝑡 = 𝜃0 + 𝜃1𝑙𝑛𝑅𝐸𝑅𝑡 + 𝜃2𝑙𝑛𝑌𝑡 + 𝜃3𝑙𝑛𝑌𝑊𝑡 + 𝜎𝑡 (10)

where, all variables remain as earlier defined; 𝜋𝑡, 𝜌𝑡 , 𝜎𝑡 are the estimated residuals

from each regression which are tested to confirm the presence of cointegration in

each model.

The residuals from these regressions represent a linear combination of these variables

at levels and it is this linear combination that should be integrated of order zero to


• 33 •

ensure a cointegrated relationship. If it is the case that the residuals are integrated of

order zero, the regression is not spurious because it implies that the variables are

cointegrated and there exists a long-run equilibrium relationship amongst the

dependent and the independent variables in each regression.

The estimated residuals from each regression are then subjected to unit root test. The

Dickey-Fuller test in the context of cointegration is known as the Engle-Granger or

Augmented Engle-Granger with no intercept and no trend term because the estimated

residuals are normally distributed with zero mean and constant variance i.e. NIID

(0,1). Hence the estimated residuals are tested for unit root using the model below:

(1 − 𝐿)𝜖𝑡 = 𝜔1𝜖𝑡−1 + 𝜇𝑡 (11)

where, 𝐿 is the lag operator; 𝜖𝑡−1 is a one period lag of the estimated residual; and 𝜇𝑡

is a stochastic error term. The decision of whether a cointegration exists between the

regressor and the regressand in each model depends on the estimates of 𝜔1. Hence,

we test the following hypothesis using the appropriate critical value:

Ho: : 𝜔1 = 0

Ha: : 𝜔1 < 0

The Mackinnon critical values for cointegration used in testing this hypothesis are

separately computed at 1%, 5% and 10% level of significance using this response

surface regression:5

𝐶𝑘(𝑝) = 𝛽∞ + 𝛽1𝑇𝑘−1 + 𝛽2𝑇𝑘

−2 + 𝑒𝑘 (12)

where 𝐶𝑘(𝑝) denote the estimated 𝑝% quantile from the 𝑘𝑡ℎ experiment, 𝑇𝑘denotes

the sample size, 𝛽∞is an estimate of the asymptotic critical value for a test at level 𝑝,

𝛽1and 𝛽2determine the shape of the response surface for finite values of 𝑇. 6

Using the Mackinnon critical values computed, the variables will be cointegrated if

the null hypothesis is rejected in favour of the alternative which implies that the

estimated residual is unit root.

After confirming if the variables in each of the three models are cointegrated, we

estimate an error correction mechanism model for the import demand, export supply

and trade balance equations. The error correction mechanism model is usually

formulated as follows;

(1 − 𝐿)𝑌𝑡 = 𝜑0 + 𝜑2(1 − 𝐿)𝑙𝑛𝑍𝑡 + 𝜑0𝐸𝐶𝑀𝑡−1 + 𝑣𝑡 (13)

where, (1 − 𝐿) is the first difference operator; 𝐸𝐶𝑀 is the estimated residual from the

cointegration regression. 𝑌𝑡, is the dependent variable which includes 𝑙𝑛𝑀𝑡, 𝑙𝑛𝑋𝑡,

𝑙𝑛𝑇𝐵𝑡 for the import, export and trade balance equation respectively;

and𝑍𝑡 represents the exogenous variables from each equation.

5 see MacKinnon (2010), Critical Values for Cointegration Test.

6 The response surface estimates of critical values are provided in a table in appendix E.


• 34 •

Equation (13) is used to analyse the short-run relationship between the dependent and

independent variables and the estimated coefficients give the short-run impact. The

coefficient of interest in this regression is 𝜑 which gives the speed of adjustment to a

long-run equilibrium relationship implied by cointegration regression when there is a

shock to this equilibrium in the short-run. A negative and statistically significant

𝜑 imply there is an adjustment to this long-run equilibrium relationship.

Although regressing a non-stationary variable on another non-stationary variable may

not be spurious because they are cointegrated, the standard errors from such

regressions are however potentially inefficient due to serial correlation and

endogeneity problem. Hence, we employ the Newey-west technique to correct the

OLS standard errors. Accordingly, the optimum lag length required for the Newey-

West regression is calculated using the truncation lag determination rule of thumb of

0.75T1/3

.

Also, the ECM model will be subjected to a series of diagnostic tests to confirm if

one or more of the following assumptions of: no serial correlation, homoscedasticity,

no autoregressive conditional heteroscedasticity (ARCH) effect and no omitted

variable is/are violated. The Breusch-Godfrey serial correlation LM test is used to

test for the presence of serial correlation in the error term. In the case where this

assumption is violated, the Newey-West method of correcting the OLS standard error

is employed in order to correct for the serial correlation. For the assumption of

homoscedasticity, we employ the Breusch-Pagan/Cook-Weisberg test for

heteroscedasticity. If the null hypothesis of a constant variance is rejected, we correct

for this problem by constructing a variance-covariance matrix that provides a

consistent estimator of the matrix. This adjustment will yield a heteroscedasticity

consistent standard errors that are known as White standard errors. A test that the

assumption of no ARCH effect is satisfied in our model is conducted using the LM

test for autoregressive conditional heteroscedasticity. The Ramsey's RESET test is

employed to determine if the assumption of no omitted variables is not violated in the

ECM model.

4. Empirical Results

Table 1 presents the results of the unit root tests conducted and the results show that

𝑙𝑛𝑀, 𝑙𝑛𝑋, 𝑙𝑛𝑌, 𝑙𝑛𝑌𝑊, 𝑙𝑛𝑅𝑃𝑋 and 𝑙𝑅𝑃𝑋 are non-stationary and integrated of order

one. The 𝑙𝑛𝑇𝐵 on the other hand is integrated of order zero I(0). However, given

the weak power of the test due to the short time period annual data available to us, for

the purpose of this study, 𝑙𝑛𝑇𝐵 was treated as an I(1) series.

Table 1. Unit Root test results for Aggregate Variables

Level First Difference

𝐼𝑚𝑝𝑜𝑟𝑡 -1.757 -6.348* I(1)

𝐸𝑥𝑝𝑜𝑟𝑡(𝐴) -1.848 -7.427* I(1)


• 35 •

𝐷 𝐼𝑛𝑐𝑜𝑚𝑒 1.151 -5.254* I(1)

𝑊 𝐼𝑛𝑐𝑜𝑚𝑒 -1.238 -4.224* I(1)

𝑅𝑃𝑀 -1.984 -6.218* I(1)

𝑅𝑃𝐸(𝐴) -0.940 -6.142* I(1)

𝑇𝑟𝑎𝑑𝑒 𝐵𝑎𝑙 -4.019* -6.858* I(0)

𝑅𝐸𝐸𝑅(𝐴) -2.487 -3.414** I(1)

NB: *, **, *** indicate statistical significance at 1%, 5%, and 10% level of significance respectively.

* (A) denotes Augmented Dickey-Fuller test.

*Critical values for model with constant are: -3.730 (1%), -2.992(5%) and -2,626(10%).

Having established that the variables are of the same order of integration, a

cointegration test was conducted, a cointegration analysis was done using the Engel-

Granger OLS-based cointegration technique. Equations (3) and (4) were estimated

using the OLS method and the estimated residuals from each equation were subjected

to a unit root test using equation (8). Also, to analyse the short-run dynamics and the

long-run dis-equilibrium, we estimated an error correction mechanism (ECM) model

to reconcile the short-run behaviour of import and export with their long-run

behaviour. The ECM models were subjected to rigorous diagnostic test to determine

if they are well specified and fit for the purpose of this study. The results of the tests

are reported in (Table 2).

Table 2. Cointegration and ECM Result for the Import and Export Function

Variables Import Function Export Function

Long-Run

Const.

-13.5783

(7.2649)

-21.7770

(15.7059)

Income 0.6026*

(0.2324)

0.8451***

(0.5011)

Relative price -0.6456*

(0.2406)

-0.0979

(0.1183)

Dickey-Fuller test -5.60* -7.54*

R2 0.7732 0.3587

Short-run

Const.

0.0145

(0.0495)

0.0119

(0.0526)

Income -0.3661

(0.7557)

-0.4356

(1.6850)


• 36 •

Relative price -0.4215*

(0.1508)

-0.1767*

(0.0729)

ECM(t-1) -0.9254*

(0.1875)

-0.8323*

(0.1101)

F-stat 14.14 20.30

Diagnostic test

Heteroscedasticity 𝑥21] 5.27 0.26

Serial correlation

𝑥2[2]

3.23 5.246

ARCH TEST 𝑥2[2] 0.028 1.836

OV test F [3,21] 2.19 0.67

NB: *, **, *** denotes statistical significance at 1%, 5%, and 10% level of significance respectively.

-The t critical value with 26 degree of freedom for a two tail test are: 1%(±2.479) 5%(± 2.056) and

10%(±1.706) respectively.

-The standard errors reported are corrected for serial correlation using Newey-West regression.

-The optimum lag length used in the Newey-West regression is approximately 2, calculated using the

truncation lag determination rule of thumb of 0.75T1/3.

-Dickey Fuller test is the t(tau) value for the unit root test conducted for each residual.

-Income in the import function is the domestic income while income in the export function is the world

income.

-The MacKinnon critical value for cointegration test for three I(1) series with no trend are: -4.829(1%),

-4.047(5%), and -3.670(10%).

The Dickey-Fuller test in each table is the t(tau) value from the unit root test

conducted for each set of residuals, while the estimated coefficients are the long-run

elasticities. The OLS standard errors are not reported because the series used are non-

stationary which implies that the regression suffers from serial correlation and

endogeneity problems. Hence, we employed the Newey-west technique to correct for

these problems.

The estimated t(=tau) value, from the unit-root test for the aggregate import demand

and export supply equation residuals are -5.60 and -7.54 respectively, which are both

significant at 1% level using the MacKinnon critical values for cointegration. These

stationary residuals imply the existence of cointegration, which suggest a long-run

equilibrium relationship amongst the import and export function variables.

It is also of interest to determine how the volume of imports and exports respond to

changes in relative prices and income in the long-run which are provided by the long-

run elasticities. In terms of the aggregate relative price elasticities, both signs are

consistent with economic theory. However, only the price elasticities of demand for

imports is statistically significant in the long-run.

From our estimation, if the Naira falls by 1%, it is expected that Nigerians will

reduce their demand for foreign goods by 0.65% which is significant at all


• 37 •

conventional levels. This decline in demand for foreign goods, everything else

constant, is more likely to result into inflation than having a positive impact on the

economy. This is because Nigeria import most of the raw materials used for domestic

production and this increase in the cost of importation will increase the cost of

domestic production which will results into inflation in the long-run.

More significant in our analysis in the long-run are the income elasticities. The

domestic and world income suggest an inelastic and significant relation both on the

volume of imports and exports in the long-run. Such that in reaction to a similar 1%

increase in domestic income, Nigerian imports from the rest of the world will be

increased by 0.60% while exports to the world will also increase by 0.85%. The value

of the adjusted R2 for the import and export functions of 0.77 and 0.36 respectively,

indicates that variation in relative prices and incomes explain about 77% of the

variation in imports and 36% of the variations in exports. The economic implication

here is that, on average, in the long-run, income has a more significant and

dominating effects on the trade than prices. Also, the import functions are more fit

for the purpose of this study than the export function.

The short-run elasticities estimated and the results of the regression diagnostic tests

are reported in (Table 3). The diagnostic tests result reveals that the import demand

specification is heteroscedastic, which implies that the standard errors from this OLS

regression are inefficient. This was corrected using the robust option to obtain

heteroscedastic consistent standard errors reported in the table.

Unlike the long-run analysis, only price has a significant impact on imports and

exports in the short-run. In response to a 1% fall of the Naira, Nigeria’s import is

expected to decrease by 0.42% while Nigeria’s export supply to the world is expected

to increase by 0.17% which are both statistically significant at 5%. The economic

implication from our estimates is that price matters in the short-run but not in the

long-run for exports.

The error correction mechanism coefficient of the import and export functions,

though negative and significant at the 5%, indicate the rapid adjustment to shocks in

the short-run which is implausible in international trade. The coefficient suggests

that when the volume of imports or exports deviates from their equilibrium value

given by the cointegration regression, all of this deviation will be corrected for within

a year. These instantaneous adjustments to shocks within one year appears

implausibly fast and assumes no adjustment is required to attain long-run equilibrium

indicating that this model may not reflect the reality of Nigeria’s trading

relationships. Therefore, our estimates will be interpreted with utmost cautions to

avoid misleading inferences.

Having laid the background for our analysis, we proceed to testing the M-L condition

using the relative price elasticities. In acknowledgement of the insignificance of the

relative price elasticity in the long-run, we test for the M-L condition based on the

existence of a long-run equilibrium relationship amongst the variables. Also, bilateral

M-L condition tests are conducted while admitting that most of the price elasticities


• 38 •

are insignificant in the long-run with reverse signs which might give unreliable

results. The results for the M-L condition tests are reported in (Table 3).

The result from testing the null hypothesis (that the sum of the price elasticities is

equals to one in absolute terms) shows that we cannot reject the null at a 5% level of

significance. The calculated t-value and prob value for rejecting the null are -0.957

and 0.1685. Hence, we tentatively conclude that the condition is not satisfied for

Nigeria in this study. This result corroborates findings from Loto (2011). However,

our lack of support for the M-L condition could be because the estimated export price

elasticities are insignificant in the long-run. Overall, we tentatively reject the M-L

condition for Nigeria due to the various limitations from our estimation and models.

Table 3. Test of Marshall-Lerner Condition

Trading Partners t-values

(prob)

Decision Rule

World -0.957

(0.1685)

Not satisfied

NB: *, **, *** denotes statistical significance at 1% , 5%, and 10% level of significance respectively,

and -The prob value for rejecting the null are reported in the brackets.

The trade balance analysis was done to examine the long-run and the short-run

relationship between the trade balance and the real exchange rate using the Engle-

Granger OLS-based cointegration technique and error correction mechanism (ECM)

modelling technique. The results reported in table 9 confirm the existence of a long-

run equilibrium relationship between the real effective exchange, domestic income,

foreign income and the trade balance in Nigeria. However, the elasticities indicate

that real effective exchange rate do not have significant impact on the trade balance

in the long-run. This provides support to our findings from investigating the M-L

condition that devaluation will not improve the trade balance in Nigeria in the long-

run.

In addition, the lower panel of table 4 presents the results of the regression diagnostic

tests and the ECM regression which captures the short-run dynamics. The regression

diagnostic test results are satisfactory, but the insignificant short-run real exchange

rate elasticities imply that devaluing the Naira will have no impact on the trade

balance in the short-run. The error correction model coefficient of -0.85 is

statistically significant at 5% level of significance. This is similar to the estimated

speed of adjustment for the aggregate import and export function. This suggests that

this trade balance ECM model suffers from aggregation bias and not relying on these

estimates might led to misleading conclusions.

However, our finding from this empirical analysis suggests that devaluation cannot

improve the trade balance both in the long-run and also in the short-run for Nigeria.

This is consistent with the finding of Ogundipe, Ojeaga, and Ogundipe (2013), Eke,

Eke and Obafemi (2015), and Olaniyi (2019).


• 39 •

Table 4. Cointegration and ECM Regression Results for the Trade Balance Analysis

Variables Cointegration Regression ECM Regression

Const. -14.4645

(10.1927)

4.3994

(6.3147)

Income -0.1762

(0.2454)

1.3143

(0.7633)

Relative price -0.1459

(0.1155)

-0.2165

(0.1189)

World Income 0.6769

(0.5346)

-0.1422

(0.2018)

D. F. Test -4.12*

ECM(t-1) -0.8525*

(0.2012)

R2 0.1891

F-stat 5.86

Diagnostic test

Heteroscedasticity 𝑥2[1] 0.09

Serial correlation 𝑥2[2] 1.643

ARCH TEST 𝑥2[2] 2.890

OV test F [3,21] 0.60

NB:-The cointegration standard errors reported are corrected for serial correlation using Newey-West

regression.

-The optimum lag length used in the Newey-West regression is 2, calculated using the truncation lag

determination rule of thumb of 0.75T1/3.

-Dickey Fuller test is the t(tau) value for the unit root test conducted for each residual.

-The MacKinnon critical value for cointegration test for four I(1) series with no trend are: -5.312(1%), -

4.497(5%), and -4.104(10%).

-A decrease of the real exchange rate implies a devaluation of the Naira.

-The reported ECM standard errors are corrected for violation of the critical assumptions when

necessary.

5. Conclusion and Policy Implication

The notion that a devaluation will improve the trade balance in the long run has for

decades been a fundamental tenet and has formed the basis of several empirical

research. Several studies have analysed and assessed the validity of this tenet using

data from different countries and using different econometric techniques with mixed

and conflicting results. This is also the case in Nigeria where there is no consensus as

to what the impact of a devaluation will have on the trade balance in Nigeria in the


• 40 •

long-run despite several decades of research. Hence the need for a re-examination

given the Nigeria’s current economic situation.

This paper re-examined this contentious issue by investigating the Marshall-Lerner

(M-L) condition for Nigeria. It also examined the long and the short-run relation

between the trade balance in Nigeria and the real exchange rate. The results from

testing for the M-L condition failed to find support for the condition in Nigeria.

Hence, it was tentatively concluded that the M-L condition does not appear to hold

persuasively for Nigeria. In addition, the empirical analysis of the relationship that

exists between the trade balance and the real exchange rate suggests that either in

long-run or in the short-run, the real effective exchange rate does not appear to have

any significant impact on Nigeria’s trade balance.

Given these results, it was concluded that the M-L condition does not hold for

Nigeria and a real devaluation of the Naira would have no significant improvement

on the trade balance both in the short-run and in the long-run. In addition, the

findings from this research corroborate an important economic principle that income

effects dominate price effects. This is evident from the long-run analysis which

revealed that even when prices are flexible, income elasticities are larger and have

more significant effects on trade.

A suggestion for further research, based on the findings and limitations encountered

in this research, is that that further investigation of the M-L condition for Nigeria

needs to be conducted on bilateral basis. This allows for the heterogeneity in bilateral

trading transaction to be captured which will be very informative for trade policy

purpose. Also, more can be done on this literature for Nigeria by conducting research

that analyses oil exports separately from non-oil exports, covering longer period

beyond 1986, to adequately account for the role of oil exports on the trade balance in

Nigeria. In addition, a policy recommendation from the findings is that monetary

authorities should employ other policy instrument to improve the trade balance as the

Nigeria trade structure does not support a devaluation improving the trade balance of

the nation.

References

Alexander, S.S. (1952) ‘Effects of a Devaluation on a Trade Balance’, International

Monetary Fund, 2 (2), pp. 263-278, JSTOR [Online]. Available at:

http://www.jstor.org/stable/3866218 (Accessed: 6 Jun 2016).

Anoke, C.I., Odo, S.I, & Ogbonna, B.C. (2016) ‘Effect of Exchange Rate

Depreciation on Trade Balance in Nigeria’, Journal of Humanities and Social

Science, 21(3), pp. 72-81. [Online]. Available at: www.iosrjournals.org

(Accessed: 17 Jun 2016).


• 41 •

Appleyard, D.R. & Field, A.J. (2001) ‘Price Adjustment and Balance-of-Payments

Disequilibrum’4th edn., International Economics. Boston, Mass.: Boston,

Mass. : McGraw-Hill/Irwin, pp. 527-551.

Arize, C.A. (1994) ‘Cointegration Test of a Long-run Relation Between the Real

Effective Exchange Rate and the Trade Balance’, International Economic

Journal, 8(3), pp. 1-9, [Online] DOI: 10.1080/10168739400000001

(Accessed: 4 Apr 2016).

Bahmani-Oskooee, M. (1991) ‘Is there a Long-run Relation Between the Trade

Balance and the real Effective Exchange Rate of LDCs?’, Economic Letter,

36, Elsevier Science Publishers, pp.403-407.

Bahmani-Oskooee, M. & Alse, J. (1994) ‘Short-run versus Long-run Effects of

Devaluation: Error-Correction Modelling and Cointegration’, Eastern

Economic Journal, 20(4), pp. 453-464, JSTOR [Online]. Available at:

http://www.jstor.org/stable/40325598 (Accessed: 22 Apr 2016).

Bahmani-Oskooee, M. (1998) ‘Cointegration Approach to Estimate the Long-run

Trade Elasticities in LDCs’, International Economics Journal, 12(3), pp. 89-

96, [Online] DOI: http://dx.doi.org/10.1080/10168739800000031 (Accessed:

21 Mar 2016).

Bahmani M., Harvey, H. & Hegerty, S.W. (2013) ‘Empirical Tests of the Marshall-

Lerner Condition: A literature Review’, Journal of Economics Studies, 40(3),

pp. 411-433, [Online] DOI:

http://dx.doi.org/10.1108/01443581311283989 (Accessed: 21 Mar

2016).

Boyd, D., Caporale, G.M. & Smith, R. (2001) ‘Real Exchange Rate Effects on the

Balance of Trade: Cointegration and the Marshall-Lerner Condition’

International Journal of Finance and Economics, 6(3), pp. 187-200.

Central Bank of Nigeria Statistical Bulletin, (2016), External Sector Statistics,

Available at: https://www.cbn.gov.ng/documents/Statbulletin.asp

Dornbusch, R. (1975) ‘Exchange Rate and Fiscal Policy in a Popular Model of

International Trade’, The American Economic Review, 65(5), pp. 859-871,

JSTOR [Online]. Available at: http://www.jstor.org/stable/1806625

(Accessed: 15 Aug 2016).

Eita, J.H. (2013) ‘Estimation of the Marshall-Lerner Condition for Namibia’,

International Business and Economics Research Journal, 12(5), pp. 511-518.

Eke, I. C., Eke, F.A. & Obafemi, F.N. (2015) ‘Exchange Rate Behaviour and Trade

Balances in Nigeria: An Empirical Investigation’, International Journal of

Humanities and Social Science, 5(8(1)), pp. 71-78.

Enders, W. (1995) Applied econometric time series. New York: New York: Wiley.

https://www.cbn.gov.ng/documents/Statbulletin.asp


• 42 •

Engel, R.F. & Granger, C. W. (1987) ‘Cointegration and Error Correction:

Representation, Estimation, and Testing’, Econometrica, 55(2), pp.251-276.

Evans, O. (2019). The Curious Case of Petro-Monetary Transmission Mechanism in

Oil-Producing Countries: An Analysis of the Effect of Oil Price on Inflation

in Nigeria. Iranian Economic Review. DOI: 10.22059/IER.2019.70884.

Evans, O., Adeniji, S., Nwaogwugwu, I., Kelikume, I., Dakare, O., & Oke, O. (2018).

The relative effect of monetary and fiscal policy on economic development

in Africa: a GMM approach to the St. Louis equation. BizEcons Quarterly, 2,

3-23.

Eze, T.C. & Okpala, C.S. (2014) ‘Quantitative Analysis of the Impact of Exchange

Rate Policies on Nigeria’s Economic Growth: A Test of Stability of

Parameter Estimates’, International Journal of Humanities and Social

Science, 4(7), Centre for Promoting Ideas, USA, pp. 265-272.

Goldstein, M. & Khan, M.S. (1978) ‘The Supply and Demand for Exports:

Simultaneous Approach’, The Review of economics and Statistics, 60(2), pp.

275-286, JSTOR [Online]. Available at: http://www.jstor.org/stable/1924981

(Accessed: 20 Apr 2016).

Gujarati, D. (2011) Econometric by Example, 1st edn. Palgrave Macmillan.

Hacche, G. & Townend, J.C. (1981) ‘Monetary Models of Exchange Rates and

Exchange Market Pressure: Some General Limitations and an Application to

Sterling's Effective Rate’, Reviewof world Economics, 117(4), pp. 622-637,

[Online] DOI:10.1007/BF02708114 (Accessed: 13 Aug 2016).

Himarios, D. (1989) ‘DO Devaluation Improve the Trade Balance? The Evidence

Revisited’, Economic Inquiry, Vol.XXVII, Periodicals Archive Online, pp.

143-168.

Hirschman, A.O. (1949) ‘Devaluation and the Trade Balance: A Note’, The Review

of Economics and Statistics, 31(1), pp.50-53, JSTOR [Online]. Available at:

http://www.jstor.org/sici?sici (Accessed: 16 Jun 2016).

Igue, N.N. & Ogunleye, T.S. (2014) ‘Impact of Real Exchange Rate on Trade

Balance in Nigeria’, African Development Review, 26(2), Blackwell

Publishing Ltd. pp. 347-358.

International Monetary Fund, (2016) ‘IMF DATA’, Available at:

http://www.imf.org/en/Data.

Lerner, A.P. (1944) ‘Economics of Control: Principles of Welfare Economics’. The

Macmillan Company. N.Y.

Loto, M.A. (2011) ‘Does Devaluation Improve the Trade Balance of Nigeria? (A

Test of the Marshall-Lerner Condition)’, Journal of Economics and

International Finance, 3(11), pp. 624-633, [Online]. Available at:

http://www.academicjournals.org/JEIF (Accessed: 04 Apr 2016).


• 43 •

Marshall, A. (1923) ‘Money, credit and commerce’, London, Macmillan.

Miles, M.A. (1979) ‘The Effects of Devaluation on the Trade Balance and the

Balance of Payments: Some New Results’, Journal of Political Economy,

87(3), pp. 600-620. JSTOR [Online]. Available at:

http://www.jstor.org/stable/1832025 (Accessed: 16 Jun 2016).

Odili, O. (2014) ‘Exchange Rate and Balance of Payment: An Autoregressive

Distributed Lag (ARDL) Econometric Investigation on Nigeria’, Journal of

Economics and Finance, 4(6), pp. 21-30, ISOR [Online]. Available at:

http://www.iosrjournals.org (Accessed: 7 Apr 2016).

Ogbonna, B.C. (2011) ‘The Impact of Exchange Rate Variations on Trade Balance:

Evidence from Nigeria, 1970-2005’, JORIND, 9(2), pp. 393-403, [Online].

Available at: www.ajol.info/journals/jorind (Accessed: 7 Apr 2016).

Ogundipe, A.A. Ojeaga, P. & Ogundipe, O.M. (2013) ‘Estimating the Long-Run

Effects of Exchange Rate Devaluation on the Trade Balance of Nigeria’,

European Scientific Journal, 9(25), pp. 233-249.

Oladipupo, A.O. & Onotaniyohuwo, F.O. (2011)’ Impact of Exchange Rate on

Balance of Payment in Nigeria’, African Research Review, 5(4), pp. 73-88,

Indexed African Journals [Online]. Available at: www.ajol.info(Accessed:

07 Apr 2016).

Olaniyi, E. (2019). The effects of US-China trade war and Trumponomics. Forum

Scientiae Oeconomia, 7(1), 47-55.

Sinha, D. (2001) ‘A note on trade elasticities in Asian countries’, The International

Trade Journal, 15(2), pp. 221-237, [Online] DOI:

http://dx.doi.org/10.1080/088539001316901042 (Accessed: 4 Apr 2016).

United Nations Conference on Trade and Development (UNCTAD) Statistics,

International Trade in goods and Services, Available at:

http://unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx?IF_

ActivePath=P,15912&sCS_ChosenLang=en.

World Bank (2016) ‘World Development Indicators’, World Bank, [Online].

Available at: http://data.worldbank.org (Accessed: 04 Jun 2016).

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ShareAlike 3.0 Unported license. To view a copy of this license, visit

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http://unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx?IF_ActivePath=P,15912&sCS_ChosenLang=en

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